Hao-Ping (Hank) Lee (Carnegie Mellon University), Wei-Lun Kao (National Taiwan University), Hung-Jui Wang (National Taiwan University), Ruei-Che Chang (University of Michigan), Yi-Hao Peng (Carnegie Mellon University), Fu-Yin Cherng (National Chung Cheng University), Shang-Tse Chen (National Taiwan University)

Audio CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is an accessible alternative to the traditional CAPTCHA for people with visual impairments. However, the literature has found that audio CAPTCHA suffers from both lower usability and security than its visual counterpart. In this paper, we propose AdvCAPTCHA, a novel audio CAPTCHA generated by using adversarial machine learning techniques. By conducting studies with people with and without visual impairments, we show that AdvCAPTCHA can outperform the status quo audio CAPTCHA in security but not usability. We demonstrate AdvCAPTCHA’s feasibility of providing detection of malicious attacks. We also present an evaluation metric, thresholding, to quantify the trade-off between usability and security for audio CAPTCHA design. Finally, we discuss approaches to the real-world adoption of AdvCAPTCHA.

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Towards Precise Reporting of Cryptographic Misuses

Yikang Chen (The Chinese University of Hong Kong), Yibo Liu (Arizona State University), Ka Lok Wu (The Chinese University of Hong Kong), Duc V Le (Visa Research), Sze Yiu Chau (The Chinese University of Hong Kong)

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Modeling End-User Affective Discomfort With Mobile App Permissions Across...

Yuxi Wu (Georgia Institute of Technology and Northeastern University), Jacob Logas (Georgia Institute of Technology), Devansh Ponda (Georgia Institute of Technology), Julia Haines (Google), Jiaming Li (Google), Jeffrey Nichols (Apple), W. Keith Edwards (Georgia Institute of Technology), Sauvik Das (Carnegie Mellon University)

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Power to the Data Defenders: Human-Centered Disclosure Risk Calibration...

Kaustav Bhattacharjee, Aritra Dasgupta (New Jersey Institute of Technology)

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EyeSeeIdentity: Exploring Natural Gaze Behaviour for Implicit User Identification...

L Yasmeen Abdrabou (Lancaster University), Mariam Hassib (Fortiss Research Institute of the Free State of Bavaria), Shuqin Hu (LMU Munich), Ken Pfeuffer (Aarhus University), Mohamed Khamis (University of Glasgow), Andreas Bulling (University of Stuttgart), Florian Alt (University of the Bundeswehr Munich)

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